Linear Optimal Runoff Aggregate (LORA)

LORA is a new global runoff dataset and its associated uncertainty with monthly temporal resolution covering the period 1980-2012 with 0.5 degree latitude and longitude resolution.

A weighting approach was applied to optimally combine available gridded runoff datasets from the eartH2Observe project. The weighting was constrained with streamflow observational records from various source. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance.

We perform out-of-sample tests to examine the success of the dissimilarity approach and we confirm that the weighted product performs better than its 11 constituents products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly time scales, and includes time variant uncertainty, for the period 1980 – 2012 on a 0.5o grid.

The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents well the seasonal runoff cycle for most of the globe. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and is freely available for download.

This dataset was produced by Dr Sanaa Hobeichi as part of her PhD thesis and of the Centre of Excellence for Climate System Science "The role of land surface forcing and feedbacks for regional climate" research program.